Particle Flow Gaussian Sum Particle Filter
Signal Processing
2023-03-23 v2
Abstract
Particle flow Gaussian particle flow (PFGPF) uses an invertible particle flow to generate a proposal density. It approximates the predictive and posterior distributions as Gaussian densities. In this paper, we use bank of PFGPF filters to construct a Particle flow Gaussian sum particle filter (PFGSPF), which approximates the predictive and posterior as Gaussian mixture model. This approximation is useful in complex estimation problems where a single Gaussian approximation is not sufficient. We compare the performance of this proposed filter with PFGPF and others in challenging numerical simulations.
Keywords
Cite
@article{arxiv.2211.05104,
title = {Particle Flow Gaussian Sum Particle Filter},
author = {Karthik Comandur and Yunpeng Li and Santosh Nannuru},
journal= {arXiv preprint arXiv:2211.05104},
year = {2023}
}
Comments
Accepted in ICASSP 2023